import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    checkpoints.load_tax_checkpoint(a__ )
  _UpperCamelCase           =    flatten_dict(a__ )
  return flax_params


def 				lowerCAmelCase__       (			a__ )      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    {}

  _UpperCamelCase           =    {
      "token_embedder": "embeddings",
      "encoder_norm": "layernorm",
      "kernel": "weight",
      ".out": ".output",
      "scale": "weight",
      "embedders_0.pos_embedding": "row_embedder.weight",
      "embedders_1.pos_embedding": "column_embedder.weight",
  }

  _UpperCamelCase           =    {
      "query": "attention.query",
      "key": "attention.key",
      "value": "attention.value",
      "output.dense": "output",
      "encoder_decoder_attention.o": "encoder_decoder_attention.attention.o",
      "pre_self_attention_layer_norm": "self_attention.layer_norm",
      "pre_cross_attention_layer_norm": "encoder_decoder_attention.layer_norm",
      "mlp.": "mlp.DenseReluDense.",
      "pre_mlp_layer_norm": "mlp.layer_norm",
      "self_attention.o": "self_attention.attention.o",
      "decoder.embeddings.embedding": "decoder.embed_tokens.weight",
      "decoder.relpos_bias.rel_embedding": "decoder.layer.0.self_attention.attention.relative_attention_bias.weight",
      "decoder.decoder_norm.weight": "decoder.final_layer_norm.weight",
      "decoder.logits_dense.weight": "decoder.lm_head.weight",
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _UpperCamelCase           =    ".".join(key[1:] )

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _UpperCamelCase           =    new_key.replace(a__    ,     a__ )

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _UpperCamelCase           =    new_key.replace(a__    ,     a__ )

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _UpperCamelCase           =    re.sub(r"layers_(\d+)"    ,     r"layer.\1"    ,     a__ )
        _UpperCamelCase           =    new_key.replace("encoder"    ,     "encoder.encoder" )

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _UpperCamelCase           =    re.sub(r"layers_(\d+)"    ,     r"layer.\1"    ,     a__ )

      _UpperCamelCase           =    flax_dict[key]

  _UpperCamelCase           =    {}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _UpperCamelCase           =    torch.from_numpy(converted_dict[key].T )
    else:
      _UpperCamelCase           =    torch.from_numpy(converted_dict[key] )

  return converted_torch_dict


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__=False    ,     a__=False )      ->List[str]:




  '''simple docstring'''

  _UpperCamelCase           =    get_flax_param(a__ )

  if not use_large:
    _UpperCamelCase           =    PixaStructVisionConfig()
    _UpperCamelCase           =    PixaStructTextConfig()
  else:
    _UpperCamelCase           =    PixaStructVisionConfig(
        hidden_size=1_536    ,     d_ff=3_968    ,     num_attention_heads=24    ,     num_hidden_layers=18 )
    _UpperCamelCase           =    PixaStructTextConfig(hidden_size=1_536    ,     d_ff=3_968    ,     num_heads=24    ,     num_layers=18 )
  _UpperCamelCase           =    PixaStructConfig(
      vision_config=encoder_config.to_dict()    ,     text_config=decoder_config.to_dict()    ,     is_vqa=a__ )

  _UpperCamelCase           =    PixaStructForConditionalGeneration(a__ )

  _UpperCamelCase           =    rename_and_convert_flax_params(a__ )
  model.load_state_dict(a__ )

  _UpperCamelCase           =    AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer" )
  _UpperCamelCase           =    PixaStructImageProcessor()
  _UpperCamelCase           =    PixaStructProcessor(image_processor=a__    ,     tokenizer=a__ )

  if use_large:
    _UpperCamelCase           =    4_096

  _UpperCamelCase           =    True

  # mkdir if needed
  os.makedirs(a__    ,     exist_ok=a__ )

  model.save_pretrained(a__ )
  processor.save_pretrained(a__ )

  print("Model saved in {}".format(a__ ) )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()
   parser.add_argument('''--t5x_checkpoint_path''', default=None, type=str, help='''Path to the original T5x checkpoint.''')
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--use_large''', action='''store_true''', help='''Use large model.''')
   parser.add_argument('''--is_vqa''', action='''store_true''', help='''Use large model.''')
   lowerCamelCase__  =					parser.parse_args()

   convert_pixastruct_original_pytorch_checkpoint_to_hf(
       args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
   )





from __future__ import annotations


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->bool:




  '''simple docstring'''

  _UpperCamelCase           =    get_failure_array(a__ )

  # 2) Step through text searching for pattern
  _UpperCamelCase      ,						_UpperCamelCase           =    0, 0  # index into text, pattern
  while i < len(a__ ):
    if pattern[j] == text[i]:
      if j == (len(a__ ) - 1):
        return True
      j += 1

    # if this is a prefix in our pattern
    # just go back far enough to continue
    elif j > 0:
      _UpperCamelCase           =    failure[j - 1]
      continue
    i += 1
  return False


def 				lowerCAmelCase__       (			a__ )      ->list[int]:




  '''simple docstring'''

  _UpperCamelCase           =    [0]
  _UpperCamelCase           =    0
  _UpperCamelCase           =    1
  while j < len(a__ ):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCamelCase           =    failure[i - 1]
      continue
    j += 1
    failure.append(a__ )
  return failure


if __name__ == "__main__":
   # Test 1)
   lowerCamelCase__  =					'''abc1abc12'''
   lowerCamelCase__  =					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
   lowerCamelCase__  =					'''alskfjaldsk23adsfabcabc'''
   assert kmp(pattern, texta) and not kmp(pattern, texta)

   # Test 2)
   lowerCamelCase__  =					'''ABABX'''
   lowerCamelCase__  =					'''ABABZABABYABABX'''
   assert kmp(pattern, text)

   # Test 3)
   lowerCamelCase__  =					'''AAAB'''
   lowerCamelCase__  =					'''ABAAAAAB'''
   assert kmp(pattern, text)

   # Test 4)
   lowerCamelCase__  =					'''abcdabcy'''
   lowerCamelCase__  =					'''abcxabcdabxabcdabcdabcy'''
   assert kmp(pattern, text)

   # Test 5)
   lowerCamelCase__  =					'''aabaabaaa'''
   assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]




